def test_prf_vs_aperture_photometry():
"""Is the PRF photometry result consistent with simple aperture photometry?"""
tpf_fn = get_pkg_data_filename(
"data/ktwo201907706-c01-first-cadence.fits.gz")
tpf = fits.open(tpf_fn)
col, row = 173, 526
prf = KeplerPRF(channel=tpf[0].header['CHANNEL'],
column=col,
row=row,
shape=tpf[1].data.shape)
fluxo, colo, rowo, _ = models.get_initial_guesses(data=tpf[1].data,
X=prf.x,
Y=prf.y)
bkgo = np.mean(tpf[1].data)
aperture_flux = tpf[1].data.sum() - bkgo
prior = JointPrior(GaussianPrior(mean=fluxo, var=math.sqrt(fluxo)),
UniformPrior(lb=prf.x[0], ub=prf.x[-1]),
UniformPrior(lb=prf.y[0], ub=prf.y[-1]),
GaussianPrior(mean=bkgo, var=bkgo))
logL = PoissonPosterior(tpf[1].data, prf.evaluate, prior=prior)
fitresult = logL.fit((fluxo, colo, rowo, bkgo))
prf_flux, prf_col, prf_row, prf_bkg = fitresult.x
assert np.isclose(prf_col, col + 9, rtol=1e-3)
assert np.isclose(prf_row, row + 9, rtol=1e-3)
assert np.isclose(prf_bkg, np.percentile(tpf[1].data, 10), rtol=0.1)
assert np.isclose(aperture_flux, prf_flux, rtol=0.1)
def __init__(self,
scale_col=GaussianPrior(mean=1, var=0.0001),
scale_row=GaussianPrior(mean=1, var=0.0001),
rotation_angle=UniformPrior(lb=-3.1415, ub=3.1415)):
self.scale_col = self._parse_prior(scale_col)
self.scale_row = self._parse_prior(scale_row)
self.rotation_angle = self._parse_prior(rotation_angle)
def __init__(self,
col,
row,
flux=UniformPrior(lb=0, ub=1e10),
targetid=None):
self.col = self._parse_prior(col)
self.row = self._parse_prior(row)
self.flux = self._parse_prior(flux)
self.targetid = targetid
def test_prf_vs_aperture_photometry():
"""Is the PRF photometry result consistent with simple aperture photometry?"""
tpf_fn = get_pkg_data_filename(
"data/ktwo201907706-c01-first-cadence.fits.gz")
tpf = fits.open(tpf_fn)
col, row = 173, 526
prf = KeplerPRF(channel=tpf[0].header['CHANNEL'],
column=col,
row=row,
shape=tpf[1].data.shape)
scene = SceneModel(prfs=prf)
fluxo, colo, rowo, _ = get_initial_guesses(data=tpf[1].data,
ref_col=prf.col_coord[0],
ref_row=prf.row_coord[0])
bkg = mode(tpf[1].data, None)[0]
prior = JointPrior(UniformPrior(lb=0.1 * fluxo, ub=fluxo),
UniformPrior(lb=prf.col_coord[0], ub=prf.col_coord[-1]),
UniformPrior(lb=prf.row_coord[0], ub=prf.row_coord[-1]),
GaussianPrior(mean=1, var=1e-2),
GaussianPrior(mean=1, var=1e-2),
GaussianPrior(mean=0, var=1e-2),
UniformPrior(lb=bkg - .5 * bkg, ub=bkg + .5 * bkg))
logL = PoissonPosterior(tpf[1].data, mean=scene, prior=prior)
result = logL.fit(x0=prior.mean, method='powell')
prf_flux, prf_col, prf_row, prf_scale_col, prf_scale_row, prf_rotation, prf_bkg = logL.opt_result.x
assert result.success is True
assert np.isclose(prf_col, colo, rtol=1e-1)
assert np.isclose(prf_row, rowo, rtol=1e-1)
assert np.isclose(prf_bkg, np.percentile(tpf[1].data, 10), rtol=0.1)
# Test KeplerPRFPhotometry class
kepler_phot = PRFPhotometry(scene_model=scene, prior=prior)
tpf_flux = tpf[1].data.reshape(
(1, tpf[1].data.shape[0], tpf[1].data.shape[1]))
kepler_phot.fit(tpf_flux=tpf_flux)
opt_params = kepler_phot.opt_params.reshape(-1)
assert np.isclose(opt_params[0], prf_flux, rtol=0.1)
assert np.isclose(opt_params[1], prf_col, rtol=1e-1)
assert np.isclose(opt_params[2], prf_row, rtol=1e-1)
assert np.isclose(opt_params[-1], prf_bkg, rtol=0.1)